Backfilling in the permafrost: predicting pressure loss and temperature distribution along the paste backfill pipeline system
Bibliographic record
Abstract
Underground mine backfilling of permafrost open stopes with cemented paste backfill (CPB) has many challenges to overcome, including keeping the CPB unfrozen during pipeline transportation at low pumping costs and ensuring the required strength development under sub-zero temperature conditions. However, CPB strength development depends strongly on the evolution of its internal curing temperature that, in turn, is controlled by the CPB placement temperature (initial) in the stope and by the permafrost boundary temperature. Hence, it is relevant to predict the required pumping pressure and distribution of the CPB temperature along the pipeline by considering the internal and external heat exchanges and the thermorheological behaviour of the CPB. The objective of this paper is to simulate the CPB pipe flow in a fullscale pipeline distribution network anticipated for a mine located in the permafrost region of the north of Canada. For that purpose, CPB mixtures were prepared at 10°C (initial temperature expected in the backfill plant) in the laboratory with 5% of type HE Portland cement at a slump height of about 17.8 cm (corresponding to a solid concentration of about 76.3 %). The numerical simulations were performed using the non-isothermal pipe flow model of COMSOL Multiphysics® 5.2 considering a distribution network of approximately 1,600 m long that includes an outdoor section of approximately 294 m exposed to air circulating at a speed of 1 m/s and to an extreme temperature of -50°C. The permafrost temperature was taken to be -5°C. Unheated (at -5°C) and heated (at +2°C) underground openings were studied. A CPB flow velocity of 1.04 m/s and a pipe diameter of 0.1463 m were considered, as proposed by the owner of the mine project. Results indicate that the total pumping pressure is about 12 MPa (heated and unheated stopes) and the pressure gradient (𝛥𝑝/𝐿) distribution along the horizontal pipeline sections is about 13 kPa/m. A decrease in the CPB temperature was observed in the surface section, followed by an increase in the underground sections. This temperature increase can be explained by the predominance of heat transfer by convection rather than radial conduction of the heat generated by viscous dissipation. The effect of a thermal insulation of the pipeline section at the surface was also studied for comparison purposes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".